Abstract: This chapter will map the ethical and legal challenges posed by artificial intelligence (AI) in health care and suggest directions for resolving them. Section 1 will briefly clarify what AI is and Section 2 will give an idea of the trends and strategies in the United States (U.S.) and Europe, thereby tailoring the discussion to the ethical and legal debate of AI-driven health care. This will be followed in Section 3 by a discussion of four primary ethical challenges, namely (1) informed consent to use, (2) safety and transparency, (3) algorithmic fairness and biases, and (4) data privacy. Section 4 will then analyze five legal challenges in the U.S. and Europe: (1) safety and effectiveness, (2) liability, (3) data protection and privacy, (4) cybersecurity, and (5) intellectual property law. Finally, Section 5 will summarize the major conclusions and especially emphasize the importance of building an AI-driven health care system that is successful and promotes trust and the motto “Health AIs for All of Us”.Keywords: Artificial Intelligence (AI), ethical challenges, U.S. and EU law, safety and effectiveness, data protection and privacy (Less)

Abstract: For knowledge to benefit research and society, it must be trustworthy. Trustworthy research is robust, rigorous, and transparent at all stages of design, execution, and reporting. Assessment of researchers still rarely includes considerations related to trustworthiness, rigor, and transparency. We have developed the Hong Kong Principles (HKPs) as part of the 6th World Conference on Research Integrity with a specific focus on the need to drive research improvement through ensuring that researchers are explicitly recognized and rewarded for behaviors that strengthen research integrity. We present five principles: responsible research practices; transparent reporting; open science (open research); valuing a diversity of types of research; and recognizing all contributions to research and scholarly activity. For each principle, we provide a rationale for its inclusion and provide examples where these principles are already being adopted.

Abstract: Advances in personalization algorithms and other applications of machine learning have vastly enhanced the ease and convenience of our media and communication experiences, but they have also raised significant concerns about privacy, transparency of technologies and human control over their operations. Going forth, reconciling such tensions between machine agency and human agency will be important in the era of artificial intelligence (AI), as machines get more agentic and media experiences become increasingly determined by algorithms. Theory and research should be geared toward a deeper understanding of the human experience of algorithms in general and the psychology of Human–AI interaction (HAII) in particular. This article proposes some directions by applying the dual-process framework of the Theory of Interactive Media Effects (TIME) for studying the symbolic and enabling effects of the affordances of AI-driven media on user perceptions and experiences.

Abstract: Governments are being put to the test as they struggle with the fast and wide spread of COVID-19. This article discusses the compelling challenges posed by the COVID-19 pandemic by examining how this wicked problem has been managed by the South Korean government with agile-adaptive, transparent actions to mitigate the surge of COVID-19. Unlike many Western countries, South Korea has been able to contain the spread of COVID-19 without a harsh forced lockdown of the epicenter of the virus. This essay argues that an agile-adaptive approach, a policy of transparency in communicating risk, and citizens' voluntary cooperation are critical factors. It also suggests that the South Korean government learned costly lessons from the MERS failure of 2015. This essay suggests ways that Western countries can manage future wicked problems such as COVID-19 without paying too much cost and maintaining quality of life in open and free societies.

Abstract: Machine learning, artificial intelligence, and other modern statistical methods are providing new opportunities to operationalise previously untapped and rapidly growing sources of data for patient benefit. Despite much promising research currently being undertaken, particularly in imaging, the literature as a whole lacks transparency, clear reporting to facilitate replicability, exploration for potential ethical concerns, and clear demonstrations of effectiveness. Among the many reasons why these problems exist, one of the most important (for which we provide a preliminary solution here) is the current lack of best practice guidance specific to machine learning and artificial intelligence. However, we believe that interdisciplinary groups pursuing research and impact projects involving machine learning and artificial intelligence for health would benefit from explicitly addressing a series of questions concerning transparency, reproducibility, ethics, and effectiveness (TREE). The 20 critical questions proposed here provide a framework for research groups to inform the design, conduct, and reporting; for editors and peer reviewers to evaluate contributions to the literature; and for patients, clinicians and policy makers to critically appraise where new findings may deliver patient benefit.

Abstract: Smart contract platforms such as Ethereum and Libra provide ways to seamlessly remove trust and add transparency to various distributed applications. Yet, these platforms lack mechanisms to guarantee user privacy, even at the level of simple payments, which are essential for most smart contracts.

Abstract: Management journals are currently responding to challenges raised by the “replication crisis” in experimental social psychology, leading to new standards for transparency. These approaches are spil...

Abstract: As information and communication technology has become pervasive in our society, we are increasingly dependent on both digital data and repositories that provide access to and enable the use of such resources. Repositories must earn the trust of the communities they intend to serve and demonstrate that they are reliable and capable of appropriately managing the data they hold.

Abstract: In this article, we develop the concept of Transparency by Design that serves as practical guidance in helping promote the beneficial functions of transparency while mitigating its challenges in automated-decision making (ADM) environments. With the rise of artificial intelligence (AI) and the ability of AI systems to make automated and self-learned decisions, a call for transparency of how such systems reach decisions has echoed within academic and policy circles. The term transparency, however, relates to multiple concepts, fulfills many functions, and holds different promises that struggle to be realized in concrete applications. Indeed, the complexity of transparency for ADM shows tension between transparency as a normative ideal and its translation to practical application. To address this tension, we first conduct a review of transparency, analyzing its challenges and limitations concerning automated decision-making practices. We then look at the lessons learned from the development of Privacy by Design, as a basis for developing the Transparency by Design principles. Finally, we propose a set of nine principles to cover relevant contextual, technical, informational, and stakeholder-sensitive considerations. Transparency by Design is a model that helps organizations design transparent AI systems, by integrating these principles in a step-by-step manner and as an ex-ante value, not as an afterthought.

Abstract: Artificial Intelligence (AI) has diffused into many areas of our private and professional life. In this research note, we describe exemplary risks of black-box AI, the consequent need for explainab...

Abstract: Benjamin Haibe-Kains1,2,3,4,5 ✉, George Alexandru Adam, Ahmed Hosny, Farnoosh Khodakarami, Massive Analysis Quality Control (MAQC) Society Board of Directors*, Levi Waldron, Bo Wang, Chris McIntosh, Anna Goldenberg, Anshul Kundaje, Casey S. Greene, Tamara Broderick, Michael M. Hoffman, Jeffrey T. Leek, Keegan Korthauer, Wolfgang Huber, Alvis Brazma, Joelle Pineau, Robert Tibshirani, Trevor Hastie, John P. A. Ioannidis, John Quackenbush & Hugo J. W. L. Aerts

Abstract: This conceptual paper addresses the issues of transparency as linked to artificial intelligence (AI) from socio-legal and computer scientific perspectives. Firstly, we discuss the conceptual distinction between transparency in AI and algorithmic transparency, and argue for the wider concept ‘in AI’, as a partly contested albeit useful notion in relation to transparency. Secondly, we show that transparency as a general concept is multifaceted, and of widespread theoretical use in multiple disciplines over time, particularly since the 1990s. Still, it has had a resurgence in contemporary notions of AI governance, such as in the multitude of recently published ethics guidelines on AI. Thirdly, we discuss and show the relevance of the fact that transparency expresses a conceptual metaphor of more general significance, linked to knowing, bringing positive connotations that may have normative effects to regulatory debates. Finally, we draw a possible categorisation of aspects related to transparency in AI, or what we interchangeably call AI transparency, and argue for the need of developing a multidisciplinary understanding, in order to contribute to the governance of AI as applied on markets and in society.

Abstract: A growing body of work shows that many problems in fairness, accountability, transparency, and ethics in machine learning systems are rooted in decisions surrounding the data collection and annotation process. In spite of its fundamental nature however, data collection remains an overlooked part of the machine learning (ML) pipeline. In this paper, we argue that a new specialization should be formed within ML that is focused on methodologies for data collection and annotation: efforts that require institutional frameworks and procedures. Specifically for sociocultural data, parallels can be drawn from archives and libraries. Archives are the longest standing communal effort to gather human information and archive scholars have already developed the language and procedures to address and discuss many challenges pertaining to data collection such as consent, power, inclusivity, transparency, and ethics & privacy. We discuss these five key approaches in document collection practices in archives that can inform data collection in sociocultural ML. By showing data collection practices from another field, we encourage ML research to be more cognizant and systematic in data collection and draw from interdisciplinary expertise.

Abstract: With the growing presence of algorithms and their far-reaching effects, artificial intelligence (AI) will be mainstream trends any time soon. Despite this surging popularity, little is known about ...

Abstract: Meta‐analysis has become the conventional approach to synthesizing the results of empirical economics research. To further improve the transparency and replicability of the reported results and to raise the quality of meta‐analyses, the Meta‐Analysis of Economics Research Network has updated the reporting guidelines that were published by this Journal in 2013. Future meta‐analyses in economics will be expected to follow these updated guidelines or give valid reasons why a meta‐analysis should deviate from them.

Abstract: The promise of digitalization is enormous and nowhere is it more critical than in its potential to transform food supply chain. Consumers have become more educated and are demanding real-time updat...

Abstract: Assistive technology featuring artificial intelligence (AI) to support human decision-making has become ubiquitous. Assistive AI achieves accuracy comparable to or even surpassing that of human exp...

Abstract: ObjectiveThis paper presents a theoretical model and two simulator studies on the psychological processes during early trust calibration in automated vehicles.BackgroundThe positive outcomes of aut...

Abstract: The primary purpose of this study was to examine the role of good governance practices on public trust in local government. In this study, a conceptual model was developed and tested empirically in Ethiopia by selecting Bahir Dar City Administration. The data analyses yielded the following results. All independent variables were highly influential in describing the public’s level of trust in their local government. In this case, participants who perceived the existence of transparency, accountability, and responsiveness had greater trust in the City Administration than their counterparts.

Abstract: Socially responsible investment (SRI) encompasses both ethical and financial paradigms. This systematic literature review explores three key research themes within the SRI literature, identifying a significant disconnect between themes and a fixation on the financial (as opposed to ethical) paradigm. One of the foundations of SRI is environmental, social, and governance (ESG) metrics. This review confirms the importance of ESG metrics in the SRI field, as they play two crucial roles, namely, as a proxy for sustainability performance and an enabler of the SRI market. However, there are two main issues related to ESG metrics that undermine their reliability: a lack of transparency and a lack of convergence.

Abstract: Transparency of algorithmic systems entails exposing system properties to various stakeholders for purposes that include understanding, improving, and/or contesting predictions. The machine learning (ML) community has mostly considered explainability as a proxy for transparency. With this work, we seek to encourage researchers to study uncertainty as a form of transparency and practitioners to communicate uncertainty estimates to stakeholders. First, we discuss methods for assessing uncertainty. Then, we describe the utility of uncertainty for mitigating model unfairness, augmenting decision-making, and building trustworthy systems. We also review methods for displaying uncertainty to stakeholders and discuss how to collect information required for incorporating uncertainty into existing ML pipelines. Our contribution is an interdisciplinary review to inform how to measure, communicate, and use uncertainty as a form of transparency.

Abstract: Organizations such as the EU High-Level Expert Group on AI and the IEEE have recently formulated ethical principles and (moral) values that should be adhered to in the design and deployment of artificial intelligence (AI). These include respect for autonomy, non-maleficence, fairness, transparency, explainability, and accountability. But how can we ensure and verify that an AI system actually respects these values? To help answer this question, I propose an account for determining when an AI system can be said to embody certain values. This account understands embodied values as the result of design activities intended to embed those values in such systems. AI systems are here understood as a special kind of sociotechnical system that, like traditional sociotechnical systems, are composed of technical artifacts, human agents, and institutions but—in addition—contain artificial agents and certain technical norms that regulate interactions between artificial agents and other elements of the system. The specific challenges and opportunities of embedding values in AI systems are discussed, and some lessons for better embedding values in AI systems are drawn.

Abstract: Background: As called for by the Sustainable Development Goals, governments, development partners and civil society are working on anti-corruption, transparency and accountability approaches to control corruption and advance Universal Health Coverage.Objectives: The objective of this review is to summarize concepts, frameworks, and approaches used to identify corruption risks and consequences of corruption on health systems and outcomes. We also inventory interventions to fight corruption and increase transparency and accountability.Methods: We performed a critical review based on a systematic search of literature in PubMed and Web of Science and reviewed background papers and presentations from two international technical meetings on the topic of anti-corruption and health. We identified concepts, frameworks and approaches and summarized updated evidence of types and causes corruption in the health sector.Results: Corruption, or the abuse of power for private gain, in health systems includes bribes and kickbacks, embezzlement, fraud, political influence/nepotism and informal payments, among other behaviors. Drivers of corruption include individual and systems level factors such as financial pressures, poorly managed conflicts of interest, and weak regulatory and enforcement systems. We identify six typologies and frameworks that model relationships influencing the scope and seriousness of corruption, and show how anti-corruption strategies such as transparency, accountability, and civic participation can affect corruption risk. Little research exists on the effectiveness of anti-corruption measures; however, interventions such as community monitoring and insurance fraud control programs show promise.Conclusions: Corruption undermines the capacity of health systems to contribute to better health, economic growth and development. Interventions and resources on prevention and control of corruption are essential components of health system strengthening for Universal Health Coverage.

Abstract: The increasing use of Artificial Intelligence (AI) for making decisions in public affairs has sparked a lively debate on the benefits and potential harms of self-learning technologies, ranging from the hopes of fully informed and objectively taken decisions to fear for the destruction of mankind. To prevent the negative outcomes and to achieve accountable systems, many have argued that we need to open up the “black box” of AI decision-making and make it more transparent. Whereas this debate has primarily focused on how transparency can secure high-quality, fair, and reliable decisions, far less attention has been devoted to the role of transparency when it comes to how the general public come to perceive AI decision-making as legitimate and worthy of acceptance. Since relying on coercion is not only normatively problematic but also costly and highly inefficient, perceived legitimacy is fundamental to the democratic system. This paper discusses how transparency in and about AI decision-making can affect the public’s perception of the legitimacy of decisions and decision-makers and produce a framework for analyzing these questions. We argue that a limited form of transparency that focuses on providing justifications for decisions has the potential to provide sufficient ground for perceived legitimacy without producing the harms full transparency would bring.

Abstract: Many areas of computer science research (e.g., performance analysis, software engineering, artificial intelligence, and human-computer interaction) validate research claims by using statistical significance as the standard of evidence. A loss of confidence in statistically significant findings is plaguing other empirical disciplines, yet there has been relatively little debate of this issue and its associated 'replication crisis' in computer science. We review factors that have contributed to the crisis in other disciplines, with a focus on problems stemming from an over-reliance on-and misuse of-null hypothesis significance testing. Computer science research can be greatly improved by following the steps taken by other disciplines, such as using more sophisticated evidentiary criteria, and showing greater openness and transparency through experimental preregistration and data/artifact repositories.

Abstract: Given the ethical problems with AI, it is clear that something should be done. Most AI policy initiatives therefore include ethics of AI. Today there are a lot of initiatives in this area and this should be applauded. However, it is not so clear what should be done, what precise course of action should be taken. For example, it is not so clear how to deal with transparency or bias, given the technologies as they are, existing bias in society, and divergent views on justice and fairness. There are also many possible measures to choose from: policy can mean regulation by means of laws and directives, say, legal regulation, but there are also other strategies that may or may not be connected to legal regulation, such as technological measures, codes of ethics, and education. And within regulation there are not only laws but also standards such as ISO norms. Moreover, other sorts of questions also need to be answered in policy proposals: not only what should be done, but also why it should be done, when it should be done, how much should be done, by whom it should be done, and what the nature, extent, and urgency of the problem are.